Browsing by Author "Kaçar H."

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    Cutting tool condition monitoring using surface texture via neural network
    (Association for Scientific Research, 2003) Saģlam H.; Kaçar H.
    For defining surface finish and monitoring tool wear is essential for optimisation of machining parameters and performing automated manufacturing systems. There is very close relationship between tool wear and surface finish parameters as surface roughness (Ra) and maximum depth of profile (Rt). The machined surface reflects the rate of tool wear and the plot of surface provides reliable information about tool condition. In this paper an approach for estimating Ra and Rt in milling process using the artificial neural networks is proposed. Feed-forward multi-layered neural networks, trained by the back-propagation algorithm are used. In training phase seven input parameters (v, f, d, Fx, Fy, Fz and Vb) and two output parameters are used and the network architecture is as 7×6×6×6×2. It was found that the ANN results are very close to the experimental results. The developed model can be used to define the quality of surface finish in tool condition monitoring systems.
  • No Thumbnail Available
    Item
    Effect of aging on abrasive wear of deformable aluminum alloy AA6351
    (2004) Meriç C.; Atik E.; Kaçar H.
    Special features of abrasive wear of deformable aluminum alloy AA6351 are studied as a function of aging conditions and modes of friction tests. After aging in different modes the specimens are tested for wear in a special installation by the method of "brad against disk" with the use of abrasives with different grain sizes. The effects of different speeds of sliding and loads on the wear resistance and surface roughness are studied. © 2004 Plenum Publishing Corporation.
  • No Thumbnail Available
    Item
    Effect of aging on the abrasive wear properties of AlMgSi1 alloy
    (Elsevier Ltd, 2006) Meriç C.; Atík E.; Kaçar H.
    In this paper, the wear performance of the aged AlMgSi1 alloy was investigated. Great improvements in mechanical properties of Al alloys can be achieved by suitable solution treatment and aging operations. A pin-on-disk wear machine was designed and developed for abrasive wear tests. The wear resistance was evaluated using a pin-on-disk wear testing method with a SiC abrasive paper counterface. The variation of wear volume is presented as a function of applied normal load, abrasive grit size and sliding distance for running speed. Mass losses were measured within a load range of 6.45-11 N, a sliding velocity range of 0.078-0.338 m/s and abrasive grit size of 5-30 μm. The effects of different sliding speeds and loads on wear resistance and surface roughness were also examined. It was measured amounts of mass loss and examined worn surfaces. Metal microscope was used to study the microstructures of the wear scars. Natural aged specimen observed maximum wear resistance. © 2005 Elsevier Ltd. All rights reserved.